Using Burkholder-Davis-Gundy lemma and Kolmogorov inequality, exponential stability of a class of stochastic forest evolution system in Hilbert space is discussed. A sufficient condition of exponential stability is established.

In the fourth chapter mostly introduces the basic principles of the random forest algorithm and focuses on two parts closely related with the definition of the random forest: the CART and the Bagging method at first.

第四章主要介绍了本文所采用的随机森林算法的基本原理，着重阐述与随机森林定义紧密相关的两种方法：分类回归树（CRAT）与Bagging方法。

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The details about our works are as follows: Firstly, we built ensemble classifiers based on random forest ( RF) algorithm and support vector machine ( SVM) method to identify the RNA-binding proteins by integrating various features.

本论文的具体研究工作包括以下几个方面：1、建立了基于随机森林算法和支持向量机算法的RNA结合蛋白识别的集成算法预测模型。

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The predicted errors of preserved 300 records were calculated by random forest, stochastic gradient boosting, support vector and artificial neuron network. The results show that artificial neuron network predict the most precisely.

并用随机森林、随机梯度Boosting、支持向量机和人工神经网络四种算法对预留的数据进行了预测，结果表明人工神经网络的预测误差最小。

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A new ensemble-learning algorithm-Random Forest is applied in this paper. As a consequence, the selected index system is more objective and more suitable for machine learning.

本文引进组合学习算法的新方法随机森林（Random Forest，RF）来选择指标，使得到的指标体系更加客观，更加符合机器学习的特点。

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Random forest is an ensemble classification methods developed by Leo Breiman in 2001.

随机森林是Leo Breiman于2001提出的一个组合分类算法。

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It introduces an outliers detection method based on random forest.

提出一种基于随机森林方法的异常样本（outliers）检测方法。

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A Neural Network Architecture for the Decoding of Long Constraint Length Convolutional Codes

长约束度卷积码译码器神经网络结构

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Andrej Karpathy, a PhD student at Stanford working on Deep Learning, decided to use a powerful, 140-million-parameter state-of-the-art Convolutional Neural Network to help work out why some selfies are more popular than other.

安德雷·卡帕西，斯坦福一个研究计算机深度学习的博士生，决定使用包含1.4亿个参数的强大卷积神经网络来研究为何有的自拍更受欢迎。

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In this paper, by training the convolutional neural network, the network can learn the the inner features automatically so that it can recognise these digital accurately.

本文通过训练卷积神经网络，使其自行学习样本中内在特征，以达到识别单个手写数字的目的。

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Building pattern classifiers with convolutional neural networks

基于卷积神经网络的模式分类器

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Then, a convolutional neural network is utilized to classify the cards.

在得到桥牌的位置后，应用卷积神经网络来分类桥牌。

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Incremental Convolution Neural Network and Its Application in Face Detection

增长式卷积神经网络及其在人脸检测中的应用

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The ability of neural networks to deal with time dynamic signal was improved with a new neural network architecture specializing in syllable recognition based on the time delay neural network ( TDNN) and the convolutional neural network.

为了提高人工神经网络处理动态信号能力，在时延神经网络（TDNN）和卷积神经网络（CNN）的基础上，针对孤立音节的特点，提出了一个新的网络结构，研究了其学习算法。

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Firstly, we use the variant of K-means obtain the basis vectors from the training data, then extract character feature by combining convolutional neural network. 2.

我们先利用改进的K-means聚类方法从训练数据中获得基向量，再结合卷积神经网络提取字符图像的特征。

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With the established ANN models of steam turbine power output and the power needed in circulating water system, the optimum vacuum value ( reference value) of condenser as well as the energy loss deviation of several main operation parameters from reference can be determined.

利用该模型和建立的循环水系统功耗神经网络模型可确定不同工况下的真空运行最优值（基准值），为凝汽器真空运行最优值的确定提供了一个全新的方法。

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The neural network models for steam turbine power output and power needed in cycle cooling water system are established for the reference values of vacuum value of condenser and have a high precision and reliability.

建立汽轮机发电功率和循环水系统功耗神经网络模型以确定不同工况下凝汽器真空运行最优值，实现对二者的模拟预测，有很高的精度和可靠性。

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Modeling and application of ANN models for power output of steam turbine and power consumption in circulating water system

汽轮机功率与循环水系统功耗神经网络模型的建立及其应用

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Fault Diagnosis Based on ANN in Water Circulation System

水循环系统中基于神经网络的故障诊断

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A method of implementing symbol logic inference system using recurrent multilayer perceptron neural networks is presented in this paper.

介绍一种用循环多层感知器神经网络实现符号逻辑推理系统的方法。

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Four Application Models for Artificial Neural Network and Genetic Algorithm in Case Based Reasoning Cycle

基于事例的推理循环中人工神经网络和遗传算法的4种应用模型

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